31 citations · 86 across the 12 of their papers we have counts for
16 papers
ChemTime: Rapid and Early Classification for Multivariate Time Series Classification of Chemical Sensors
Alexander M. Moore, Randy C. Paffenroth, Kenneth T. Ngo +1
Multivariate time series data are ubiquitous in the application of machine learning to problems in the physical sciences. Chemiresistive sensor arrays are highly promising in chemi…
ChemVise: Maximizing Out-of-Distribution Chemical Detection with the Novel Application of Zero-Shot Learning
Alexander M. Moore, Randy C. Paffenroth, Ken T. Ngo +1
Accurate chemical sensors are vital in medical, military, and home safety applications. Training machine learning models to be accurate on real world chemical sensor data requires…
The Pseudo Projection Operator: Applications of Deep Learning to Projection Based Filtering in Non-Trivial Frequency Regimes
Matthew L. Weiss, Nathan C. Frey, Siddharth Samsi +2
Traditional frequency based projection filters, or projection operators (PO), separate signal and noise through a series of transformations which remove frequencies where noise is…
Reconstruction of Fragmented Trajectories of Collective Motion using Hadamard Deep Autoencoders
Kelum Gajamannage, Yonggi Park, Randy Paffenroth +1
Learning dynamics of collectively moving agents such as fish or humans is an active field in research. Due to natural phenomena such as occlusion and change of illumination, the mu…
Neural Network Ensembles: Theory, Training, and the Importance of Explicit Diversity
Wenjing Li, Randy C. Paffenroth, David Berthiaume
Ensemble learning is a process by which multiple base learners are strategically generated and combined into one composite learner. There are two features that are essential to an…
A Pre-training Oracle for Predicting Distances in Social Networks
Gunjan Mahindre, Randy Paffenroth, Anura Jayasumana +1
In this paper, we propose a novel method to make distance predictions in real-world social networks. As predicting missing distances is a difficult problem, we take a two-stage app…
Blind Image Denoising and Inpainting Using Robust Hadamard Autoencoders
Rasika Karkare, Randy Paffenroth, Gunjan Mahindre
In this paper, we demonstrate how deep autoencoders can be generalized to the case of inpainting and denoising, even when no clean training data is available. In particular, we sho…
Machine Learning in LiDAR 3D point clouds
F. Patricia Medina, Randy Paffenroth
LiDAR point clouds contain measurements of complicated natural scenes and can be used to update digital elevation models, glacial monitoring, detecting faults and measuring uplift…
Image Denoising Using the Geodesics' Gramian of the Manifold Underlying Patch-Space
Kelum Gajamannage
With the proliferation of sophisticated cameras in modern society, the demand for accurate and visually pleasing images is increasing. However, the quality of an image captured by…
Bounded Manifold Completion
Kelum Gajamannage, Randy Paffenroth
Nonlinear dimensionality reduction or, equivalently, the approximation of high-dimensional data using a low-dimensional nonlinear manifold is an active area of research. In this pa…
Dimension Estimation Using Autoencoders
Nitish Bahadur, Randy Paffenroth
Dimension Estimation (DE) and Dimension Reduction (DR) are two closely related topics, but with quite different goals. In DE, one attempts to estimate the intrinsic dimensionality…
Anomaly Detection via Graphical Lasso
Haitao Liu, Randy C. Paffenroth, Jian Zou +1
Anomalies and outliers are common in real-world data, and they can arise from many sources, such as sensor faults. Accordingly, anomaly detection is important both for analyzing th…
Network Topology Mapping from Partial Virtual Coordinates and Graph Geodesics
Anura P. Jayasumana, Randy Paffenroth, Gunjan Mahindre +2
For many important network types (e.g., sensor networks in complex harsh environments and social networks) physical coordinate systems (e.g., Cartesian), and physical distances (e.…
Robust PCA for Anomaly Detection in Cyber Networks
Randy Paffenroth, Kathleen Kay, Les Servi
This paper uses network packet capture data to demonstrate how Robust Principal Component Analysis (RPCA) can be used in a new way to detect anomalies which serve as cyber-network…
Network Topology Mapping from Partial Virtual Coordinates and Graph Geodesics
Anura P. Jayasumana, Randy Paffenroth, Sridhar Ramasamy
For many important network types (e.g., sensor networks in complex harsh environments and social networks) physical coordinate systems (e.g., Cartesian), and physical distances (e.…
A Nonlinear Dimensionality Reduction Framework Using Smooth Geodesics
Kelum Gajamannage, Randy Paffenroth, Erik M. Bollt
Existing dimensionality reduction methods are adept at revealing hidden underlying manifolds arising from high-dimensional data and thereby producing a low-dimensional representati…